p. 201
–212
(12)
Radar micro-Doppler (m-D) signatures are of great potential for identifying properties of unknown targets. All the techniques developed for extracting m-D features for the past decade rely primarily on the assumption that the time series of the signal contains at least one oscillation or more during the coherent integration time or imaging time. However, many applications in real-world scenarios involve short duration signals and often require the detection and the estimation of m-D characteristics. Short duration signals may contain only a fraction of an oscillation. In this study, the authors develop two techniques to estimate the m-D parameters from a fraction of the period. In these scenarios, the coherent integration will cover only 1/4 and 1/2 of the oscillation. The performance of the proposed methods are evaluated using both synthetic and experimental data.

p. 213
–217
(5)
Micro-Doppler has played an important role in describing the micro-motion of a radar target. After analysing the characteristics of micro-Doppler induced by typical micro-motions, a technique based on time–frequency transform and Hough transform is introduced, to extract the modes and estimate the parameters of multiple frequency modulation components in micro-Doppler simultaneously, which can describe the properties of radial micro-motion. Experiments show the performance of estimation algorithm with simulating radar data.

p. 218
–223
(6)
A method to describe and estimate the characteristics of target with micro-motions based on cyclostationary theory is proposed in this study. The radar signal returned from a target with vibration or rotation can be modelled as cyclostationary, and a method based on cyclic statistics is designed to estimate the frequency and amplitude of the vibration or rotation. The experiments showed the performance of the proposed method under different signal noise ratios with simulated radar data.

p. 224
–233
(10)
This study extends the theory of the micro-Doppler effect into the multistatic domain, and considers how the multistatic micro-Doppler signature (µ-DS) will affect radar automatic target recognition (ATR). Real multistatic µ-DS of personnel targets are examined and their nature compared with theory and simulated results. It is demonstrated that the use of multistatic µ-DSs would increase the robustness of radar ATR systems to the problem of self-occlusion, where the target obscures itself. Redundancy in the multistatic µ-DS is identified, and how this may reduce the data fusion demands of multi-aspect classifiers is discussed. Also, information theory is used to demonstrate that the multistatic µ-DS contains more target information than the monostatic case. This result leads to the prediction that multi-aspect µ-DS-based radar ATR would have improved performance compared to single-aspect solutions.

p. 234
–244
(11)
The authors develop methods for the time–frequency (TF) analysis of human gait radar signals. In particular the authors demonstrate how knowledge of different motion classes can be obtained via a Markov chain model of state transitions based on the TF envelope structure associated with the motion sequence being analysed. The class-conditional knowledge thus obtained allows us to effectively extract the motion curves associated with different body parts via a non-parametric partial tracking algorithm that is coupled with an optimum Gaussian g-Snake modelling of the TF structure. The optimum segmentation of the TF structure into different half-cycles as well as the determination of the initial Doppler control points (corresponding to each half-cycle) is facilitated by a dynamic programming algorithm wherein the associated cost function involves a mean-square minimisation of the best quadratic fit to each segment together with a sparsity prior that enables us to control the smoothness of the approximation space in which the time series being analysed is effectively projected. Finally, the authors describe some of the limitations of our approach and point out future research directions that can overcome some of the difficulties associated with the complex interaction between the inherently non-linear dynamics of human gait motion and radar systems.

p. 245
–255
(11)
Rotating targets cause phase modulation of the azimuthal phase history of a synthetic aperture radar (SAR) system. The phase modulation may be seen as a time-dependent micro-Doppler (m-D) frequency. This study presents two approaches for extracting m-D features from SAR images. In order to extract m-D features from SAR images, the time domain radar return is decomposed in two separate ways. One is based on wavelet decomposition in which the returned signal is decomposed into a set of components that are represented at different wavelet scales. The components are then reconstructed by applying the inverse wavelet transform. This wavelet approach has been used in previous m-D analysis work for an inverse SAR (ISAR) system, and it is presented here in the extraction of m-D features for a SAR system. The second approach is based on adaptive chirplet decomposition combined with time–frequency analysis. This new approach is introduced as an alternative to the wavelet approach of decomposing the SAR radar return. The results from the wavelet and adaptive chirplet decomposition procedures are compared, and the chirplet-based approach establishes itself as a viable alternative. The chirplet-based method of m-D extraction has been successfully applied to SAR data scene collected by the US Navy APY-6 radar.

p. 272
–286
(15)
Joint spectral–temporal methods for target detection are not uncommon, but generally involve application of spectral processing on short sub-sections of the original time series. Usually a sliding window is applied, so that the region of truncation slides through the superset. Final analysis and interpretation are usually performed in the spectral domain. Here a new method of temporal-spectral analysis is presented. Truncation is used first in the spectral domain, and then multiple sets of resulting time series are employed for identification purposes. The method is called the ETISTI method (‘enhanced truncated interleaved spectral–temporal interferometery’), and is adapted and extended from earlier meteor studies. It uses a combination of band-pass spectral filtering, auto-correlative algorithms and time-series analysis for target detection. It especially utilises long data sets of the order of hundreds to thousands of seconds, in order to improve spectral resolution, but at the same time achieves temporal resolution of the order of seconds. Signal-to-noise levels are determined locally rather than globally, using dynamic auotocovariance methods, thereby allowing adaptive time- and range-dependent noise-level determination, and hence better target discrimination. The method works especially well for accelerating targets, and for targets obscured by ionospheric interference, lightning and intermittent RF noise.

p. 287
–297
(11)
The time–frequency representation (TFR) is a powerful tool for analysis of non-stationary signals. In the past decades, TFRs have been primarily devoted to the analysis tasks in the sense that they were introduced so as to depict time–frequency structure of time-varying signals and non-stationary processes in the time–frequency plane. Also, there has been a permanent interest in tackling decision problems by means of TFRs. In this study the authors, present a time–frequency-based detection scheme in the high-frequency surface-wave radar (HFSWR) for the detection of manoeuvring air targets in the presence of noise. Performance of the proposed method is evaluated by using both synthetic and experimental data. In addition, the proposed time–frequency detection scheme is examined in detail with various signal-to-noise ratio. This time–frequency-based detection method is then compared to the Fourier-based detector. Results clearly demonstrate that the time–frequency-based detector can significantly improve detection performance in the HFSWR, as well as add new physical insight, compared to the conventional Fourier-based detector, currently used by HFSWRs.

p. 298
–304
(7)
A new method to reject ground clutter using the Amplitude and Phase EStimation (APES) method is proposed. The theoretical approach is followed by the application of this method to the rejection of such an interference in the frame of a bistatic passive radar using digital video broadcasting – terrestrial (DVB–T) transmitters.

p. 305
–313
(9)
Noise radars represent a rapidly growing research topic owing to numerous advantages over the conventional radars. This study proposes a method for strong non-stationary jammer suppression in noise radar systems. The corrupted received signal is divided into non-overlapping segments so that the instantaneous frequency (IF) of the jammer can be approximated by a parabola within each segment. To that end, an adaptive recursive procedure is proposed. The procedure uses the polynomial-phase transform to estimate the parabola coefficients. The jammer suppression is done for each segment separately. The simulations, performed for various types of FM interferences, prove the effectiveness of the proposed method even for highly non-stationary jammers with non-polynomial phase.

p. 314
–323
(10)
For obvious reasons of cost, discretion and reliability costs, locating and tracking aerial targets under an electromagnetically completely passive paradigm, relying exclusively on illuminators of opportunity, is very appealing not only for military but also for civilian tasks. Such a passive radar system could exploit signals emitted by existing commercial television or radio stations or even satellite signals, such as the ones belonging to the global positioning system. This study considers target locating and tracking using a network of passive receivers and/or non-cooperative illuminators (a multi-static radar configuration) by making use of the Doppler shift only. A concept, the systemic approach, is used to combine and interpret information available from different sensors. Both the formalisation of the problem and the hardware and software implementation are presented. Implementation makes use of multi-component polynomial-phase signal models and genetic algorithms. For increased performance, implementation on field programmable gate array is envisaged.